Nanoparticle Preparation Using the Single Microemulsions Scheme
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nanoparticles serve the need for advanced materials with specific chemical, physical, and electronic properties. These properties can be attained by manipulating the particle size. Consequently, size control has been recognized as a key factor for selecting a nanoparticle preparation technique. (w/o) Microemulsions, or reverse micelles, have been successfully used to prepare wide variety of nanoparticles with controlled sizes. Studies showed that adjusting microemulsion and/or operation variables provides a key to controlling nanoparticle size and polydispersity. The effect of a given variable, however, relies heavily on the reactant addition scheme. The mixing of two microemulsions scheme has been widely used in the literature, and the effect of microemulsion and operation variables on intermicellar nucleation and growth was detailed. The single microemulsions reactant addition scheme, on the other hand, enables intramicellar nucleation and growth, and therefore, may lead to a different response. Moreover, studies on nanoparticle preparation using the single microemulsions scheme involved more of reactive surfactants and introduced the concept nanoparticle uptake, which pertains to the maximum colloidal concentration of nanoparticles that can be stabilized in a microemulsion system. This review looks into the mechanisms controlling nanoparticle formation and compares literature trends reported for the effect of microemulsion and operation variables on the nanoparticle size and polydispersity for the single microemulsions reactant addition scheme. Moreover, it sheds some light on nanoparticle uptake and its significance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it